
"""simple docstring"""

import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=9_9 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=4 ,							lowercase_="gelu" ,							lowercase_=0.0 ,							lowercase_=0.1 ,							lowercase_=True ,							lowercase_=5_1_2 ,							lowercase_=1_6 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_=None ,							)    ->       Dict:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_input_mask
          UpperCAmelCase          =       use_token_type_ids
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       intermediate_multiple_size
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       hidden_dropout
          UpperCAmelCase          =       attention_dropout
          UpperCAmelCase          =       weight_tying
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       type_sequence_label_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       num_labels
          UpperCAmelCase          =       num_choices
          UpperCAmelCase          =       scope




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)

          UpperCAmelCase          =       None
          if self.use_input_mask:
              UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

          UpperCAmelCase          =       None
          if self.use_labels:
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels				)

          UpperCAmelCase          =       self.get_config()

          return config, input_ids, input_mask, token_labels




      def      a_   (				self				)    ->       Any:
          return GPTNeoXJapaneseConfig(
              vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_multiple_size=self.intermediate_multiple_size ,							hidden_act=self.hidden_act ,							hidden_dropout=self.hidden_dropout ,							attention_dropout=self.attention_dropout ,							weight_tying=self.weight_tying ,							max_position_embeddings=self.max_position_embeddings ,							type_vocab_size=self.type_vocab_size ,							is_decoder=snake_case_ ,							initializer_range=self.initializer_range ,							)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()

          UpperCAmelCase          =       True

          return config, input_ids, input_mask, token_labels




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          UpperCAmelCase          =       GPTNeoXJapaneseModel(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          UpperCAmelCase          =       model(snake_case_ ,							attention_mask=snake_case_				)
          UpperCAmelCase          =       model(snake_case_				)
          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       True
          UpperCAmelCase          =       GPTNeoXJapaneseModel(snake_case_				)
          model.to(snake_case_				)
          model.eval()
          UpperCAmelCase          =       model(snake_case_ ,							attention_mask=snake_case_				)
          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          UpperCAmelCase          =       GPTNeoXJapaneseForCausalLM(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()
          UpperCAmelCase          =       model(snake_case_ ,							attention_mask=snake_case_ ,							labels=snake_case_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       True
          UpperCAmelCase          =       GPTNeoXJapaneseForCausalLM(config=snake_case_				)
          model.to(snake_case_				)
          model.eval()

          # first forward pass
          UpperCAmelCase          =       model(snake_case_ ,							attention_mask=snake_case_ ,							use_cache=snake_case_				)
          UpperCAmelCase          =       outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          UpperCAmelCase          =       ids_tensor((self.batch_size, 3) ,							config.vocab_size				)
          UpperCAmelCase          =       ids_tensor((self.batch_size, 3) ,							vocab_size=2				)

          # append to next input_ids and
          UpperCAmelCase          =       torch.cat([input_ids, next_tokens] ,							dim=-1				)
          UpperCAmelCase          =       torch.cat([input_mask, next_mask] ,							dim=-1				)

          UpperCAmelCase          =       model(snake_case_ ,							attention_mask=snake_case_ ,							output_hidden_states=snake_case_				)
          UpperCAmelCase          =       output_from_no_past["""hidden_states"""][0]
          UpperCAmelCase          =       model(
              snake_case_ ,							attention_mask=snake_case_ ,							past_key_values=snake_case_ ,							output_hidden_states=snake_case_ ,							)["""hidden_states"""][0]

          # select random slice
          UpperCAmelCase          =       ids_tensor((1,) ,							output_from_past.shape[-1]				).item()
          UpperCAmelCase          =       output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCAmelCase          =       output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(snake_case_ ,							snake_case_ ,							atol=1E-3				)				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          UpperCAmelCase          =       config_and_inputs
          UpperCAmelCase          =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict

@require_torch
class   _UpperCAmelCase				(		_A  ,      _A  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Dict				   =						(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE :				List[str]				   =						(
          {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
          if is_torch_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						False
      __SCREAMING_SNAKE_CASE :				int				   =						False
      __SCREAMING_SNAKE_CASE :				Any				   =						False
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						False




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       GPTNeoXJapaneseModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=snake_case_ ,							hidden_size=3_7				)




      def      a_   (				self				)    ->       Optional[Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(snake_case_ ,							snake_case_ ,							snake_case_				)




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(snake_case_ ,							snake_case_ ,							snake_case_				)




      def      a_   (				self				)    ->       Optional[int]:
          # This regression test was failing with PyTorch < 1.3
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_decoder()

          UpperCAmelCase          =       None

          self.model_tester.create_and_check_model_as_decoder(snake_case_ ,							snake_case_ ,							snake_case_				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(snake_case_ ,							snake_case_ ,							snake_case_				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*snake_case_				)




      @slow
      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       """abeja/gpt-neox-japanese-2.7b"""

          UpperCAmelCase          =       ["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]

          UpperCAmelCase          =       [
              """データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
              """100年後に必要とされる会社は、「人」が中心の会社です。""",
              """フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
              """国境の長いトンネルを抜けると、そこは雪国だった。""",
              """美味しい日本食といえば、やっぱりお寿司ですよね。""",
          ]

          UpperCAmelCase          =       GPTNeoXJapaneseTokenizer.from_pretrained(snake_case_				)
          UpperCAmelCase          =       GPTNeoXJapaneseForCausalLM.from_pretrained(snake_case_				)

          UpperCAmelCase          =       []
          for prompt in prompts:
              UpperCAmelCase          =       tokenizer(snake_case_ ,							return_tensors='pt'				).input_ids
              UpperCAmelCase          =       model.generate(snake_case_ ,							max_length=5_0				)
              UpperCAmelCase          =       tokenizer.batch_decode(snake_case_ ,							skip_special_tokens=snake_case_				)
              predicted_outputs += generated_string
          self.assertListEqual(snake_case_ ,							snake_case_				)





from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






